code: def find_label_issues_using_argmax_confusion_matrix(
      labels: np.ndarray,
      pred_probs: np.ndarray,
      *,
      calibrate: bool = True,
      filter_by: str = "prune_by_noise_rate",
  ) -> np.ndarray:
      """A baseline approach that uses the confusion matrix
      of ``argmax(pred_probs)`` and labels as the confident joint and then uses cleanlab
      (confident learning) to find the label issues using this matrix.
  
      The only difference between this and `~cleanlab.filter.find_label_issues` is that it uses the confusion matrix
      based on the argmax and given label instead of using the confident joint
      from :py:func:`count.compute_confident_joint
      <cleanlab.count.compute_confident_joint>`.
  
      Parameters
      ----------
      labels : np.ndarray
          An array of shape ``(N,)`` of noisy labels, i.e. some labels may be erroneous.
          Elements must be in the set 0, 1, ..., K-1, where K is the number of classes.
  
      pred_probs : np.ndarray
          An array of shape ``(N, K)`` of model-predicted probabilities,
          ``P(label=k|x)``. Each row of this matrix corresponds
          to an example `x` and contains the model-predicted probabilities that
          `x` belongs to each possible class, for each of the K classes. The
          columns must be ordered such that these probabilities correspond to
          class 0, 1, ..., K-1. `pred_probs` should have been computed using 3 (or
          higher) fold cross-validation.
  
      calibrate : bool, default=True
          Set to ``True`` to calibrate the confusion matrix created by ``pred != given labels``.
          This calibration adjusts the confusion matrix / confident joint so that the
          prior (given noisy labels) is correct based on the original labels.
  
      filter_by : str, default='prune_by_noise_rate'
          See `filter_by` argument of `~cleanlab.filter.find_label_issues`.
  
      Returns
      -------
      label_issues_mask : np.ndarray
        A boolean mask for the entire dataset where ``True`` represents a
        label issue and ``False`` represents an example that is accurately
        labeled with high confidence.
  
      """
  
      assert_valid_inputs(
          X=None, y=labels, pred_probs=pred_probs, multi_label=False
      )
      confident_joint = confusion_matrix(
          np.argmax(pred_probs, axis=1), labels
      ).T
      if calibrate:
          confident_joint = calibrate_confident_joint(
              confident_joint, labels
          )
      return find_label_issues(
          labels=labels,
          pred_probs=pred_probs,
          confident_joint=confident_joint,
          filter_by=filter_by,
      )
  
imports: import numpy
  from sklearn.metrics import confusion_matrix
  import multiprocessing
  import sys
  import warnings
  from typing import Any, Dict, Optional, Tuple, List
  from functools import reduce
  import platform
  from cleanlab.count import calibrate_confident_joint, num_label_issues, _reduce_issues
  from cleanlab.rank import order_label_issues, get_label_quality_scores
  import cleanlab.internal.multilabel_scorer
  from cleanlab.internal.validation import assert_valid_inputs
  from cleanlab.internal.util import value_counts_fill_missing_classes, round_preserving_row_totals, get_num_classes
  from cleanlab.internal.multilabel_utils import stack_complement, get_onehot_num_classes, int2onehot
  from cleanlab.typing import LabelLike
  from cleanlab.multilabel_classification.filter import find_multilabel_issues_per_class
label_desc: Use image node_id 7 for calling a global function with example usage: find_label_issues_using_argmax_confusion_matrix(labels, pred_probs) and returns: find_label_issues